@article{Dong2026, 
author = {Linyu Dong and Tao Li and Hao Li},
title = {HiFreq-DETR: A Hierarchical Framework Synergizing High-Resolution Injection and Frequency-Aware Multi-Scale Interaction for Tiny Object Detection},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {88},
number = {3},
pages = {30},
keywords = {UAV, tiny object detection, multi-scale feature interaction, frequency-selective attention, high-resolution representation learning, DETR},
url = {https://www.sciopen.com/article/10.32604/cmc.2026.083042},
doi = {10.32604/cmc.2026.083042},
abstract = {While Transformer-based detectors excel in global modeling, their efficacy in unmanned aerial vehicle (UAV)-based tiny object detection is limited by information loss during aggressive downsampling and the lack of high-frequency structural cues. To bridge this gap, we propose HiFreq-DETR, a dedicated framework that optimizes the synergy between spatial fidelity and semantic discriminability. The core innovation lies in its hierarchical information preservation strategy, which employs a ResNeSt14d backbone coupled with an  S2 spatial injection path to recover critical high-resolution structural anchors, and introduces a frequency-selective interaction module to decouple target saliency from background noise. Experimental results demonstrate the substantial value of our approach. On the VisDrone dataset, HiFreq-DETR significantly outperforms the baseline RT-DETR, achieving improvements of 3.9% in AP and 4.4% in  APS, confirming its effectiveness for tiny object detection. Furthermore, an optimized lite variant is evaluated to challenge the limits of high-efficiency processing for resource-constrained scenarios, while superior gains on the HazyDet dataset validate the model’s structural robustness in adverse aerial environments. These findings establish HiFreq-DETR as a high-fidelity and versatile solution for complex remote sensing applications.}
}